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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 483535, 21 pages
http://dx.doi.org/10.1155/2012/483535
Research Article

An Adaptive Fuzzy Min-Max Neural Network Classifier Based on Principle Component Analysis and Adaptive Genetic Algorithm

School of Information Science and Engineering, Northeastern University, Shenyang 110004, China

Received 31 August 2012; Accepted 25 October 2012

Academic Editor: Bin Jiang

Copyright © 2012 Jinhai Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. C. Bezdek and S. K. Pal, Fuzzy Models for Pattern Recognition, IEEE Press, Piscataway, NJ, USA, 1992. View at Zentralblatt MATH
  2. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  3. L. A. Zadeh, “Fuzzy logic, neural networks, and soft computing,” Communications of the ACM, vol. 37, no. 3, pp. 77–84, 1994. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Mitra and S. K. Pal, “Fuzzy sets in pattern recognition and machine intelligence,” Fuzzy Sets and Systems, vol. 156, no. 3, pp. 381–386, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Abe and M. S. Lan, “A method for fuzzy rules extraction directly from numerical data and its application to pattern classification,” IEEE Transactions on Fuzzy Systems, vol. 3, no. 1, pp. 18–28, 1995. View at Publisher · View at Google Scholar · View at Scopus
  6. J. C. Bezdek and S. K. Pal, Fuzzy Models for Pattern Recognition, IEEE Press, Piscataway, NJ, USA, 1992.
  7. G. Carpenter, S. Grossberg, and D. B. Rosen, “Fuzzy ART: an adaptive resonance algorithm for rapid, stable classification of analog patterns,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '91), vol. 2, pp. 411–416, Seattle, Wash, USA, 1991.
  8. R. J. Wai and J. D. Lee, “Adaptive fuzzy-neural-network control for maglev transportation system,” IEEE Transactions on Neural Networks, vol. 19, no. 1, pp. 54–70, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Yilmaz and Y. Oysal, “Fuzzy wavelet neural network models for prediction and identification of dynamical systems,” IEEE Transactions on Neural Networks, vol. 21, no. 10, pp. 1599–1609, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. E. Kolman and M. Margaliot, “Are artificial neural networks white boxes?” IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 844–852, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. C. F. Juang, S. H. Chiu, and S. J. Shiu, “Fuzzy system learned through fuzzy clustering and support vector machine for human skin color segmentation,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 37, no. 6, pp. 1077–1087, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. T. Hasegawa, S. Horikawa, and T. Furuhashi, “A study on fuzzy modeling of BOF using a fuzzy neural network,” in Proceedings of the 2nd International Conference on Fuzzy Systems, Neural Networks and Genetic Algorithms (IIZUKA), pp. 1061–1064, 1992.
  13. P. G. Campos, E. M. J. Oliveira, T. B. Ludermir, and A. F. R. Araújo, “MLP networks for classification and prediction with rule extraction mechanism,” in Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 2, pp. 1387–1392, July 2004. View at Scopus
  14. A. Rizzi, M. Panella, F. M. F. Mascioli, and G. Martinelli, “A recursive algorithm for fuzzy Min-Max networks,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '00), vol. 6, pp. 541–546, July 2000. View at Scopus
  15. A. Rizzi, M. Panella, and F. M. F. Mascioli, “Adaptive resolution min-max classifiers,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 402–414, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Meneganti, F. S. Saviello, and R. Tagliaferri, “Fuzzy neural networks for classification and detection of anomalies,” IEEE Transactions on Neural Networks, vol. 9, no. 5, pp. 848–861, 1998. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Rizzi, M. Panella, and F. M. F. Mascioli, “Adaptive resolution min-max classifiers,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 402–414, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Abe and M. S. Lan, “A method for fuzzy rules extraction directly from numerical data and its application to pattern classification,” IEEE Transactions on Fuzzy Systems, vol. 3, no. 1, pp. 18–28, 1995. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Tagliaferri, A. Eleuteri, M. Menegatti, and F. Barone, “Fuzzy min-max neural networks: from classification to regression,” Soft Computing, vol. 5, no. 16, pp. 69–76, 2001.
  20. M. Meneganti, F. S. Saviello, and R. Tagliaferri, “Fuzzy neural networks for classification and detection of anomalies,” IEEE Transactions on Neural Networks, vol. 9, no. 5, pp. 848–861, 1998. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Rizzi, M. Panella, F. M. F. Mascioli, and G. Martinelli, “A recursive algorithm for fuzzy Min-Max networks,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN'00), pp. 541–546, July 2000. View at Scopus
  22. P. Liu and H. Li, “Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks,” IEEE Transactions on Neural Networks, vol. 15, no. 3, pp. 545–558, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. W. Pedrycz, “Heterogeneous fuzzy logic networks: fundamentals and development studies,” IEEE Transactions on Neural Networks, vol. 15, no. 6, pp. 1466–1481, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. P. K. Simpson, “Fuzzy min-max neural networks-I: classification,” IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 776–786, 1992. View at Publisher · View at Google Scholar · View at Scopus
  25. A. V. Nandedkar and P. K. Biswas, “A fuzzy min-max neural network classifier with compensatory neuron architecture,” IEEE Transactions on Neural Networks, vol. 18, no. 1, pp. 42–54, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Gabrys and A. Bargiela, “General fuzzy min-max neural network for clustering and classification,” IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 769–783, 2000. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Quteishat, C. P. Lim, and K. S. Tan, “A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 40, no. 3, pp. 641–650, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. H. Zhang, J. Liu, D. Ma, and Z. Wang, “Data-core-based fuzzy min-max neural network for pattern classification,” IEEE Transactions on Neural Networks, vol. 22, no. 3, pp. 2339–2352, 2011. View at Publisher · View at Google Scholar
  29. H. J. Kim and H. S. Yang, “A weighted fuzzy min-max neural network and its application to feature analysis,” in Proceedings of the 1st International Conference on Natural Computation (ICNC '05), Lecture Notes on Computer Science, pp. 1178–1181, August 2005. View at Scopus
  30. M. Kallas, C. Francis, L. Kanaan, D. Merheb, P. Honeine, and H. Amoud, “Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals,” in Proceedings of the 19th International Conference on Telecommunications (ICT '12), pp. 1–5, April 2012. View at Publisher · View at Google Scholar
  31. J. D. Schaffer and A. Morishma, “An adaptive crossover mechanism for genetic algorithms,” in Proceedings of the 2nd International Conference on Genetic Algorithms, p. 3640, 1987.
  32. N. P. Jawarkar, R. S. Holambe, and T. K. Basu, “Use of fuzzy min-max neural network for speaker identification,” in Proceedings of the International Conference on Recent Trends in Information Technology (ICRTIT '11), pp. 178–182, June 2011.
  33. A. Quteishat and C. P. Lim, “A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification,” Applied Soft Computing Journal, vol. 8, no. 2, pp. 985–995, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. Z. Huaguang and Q. Yongbing, “Modeling, identification, and control of a class of nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 2, pp. 349–354, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  35. H. Zhang, Q. Gong, and Y. Wang, “Delay-dependent robust H∞ control for uncertain fuzzy hyperbolic systems with multiple delays,” Progress in Natural Science, vol. 18, no. 1, pp. 97–104, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. H. G. Zhang, Y. C. Wang, and D. R. Liu, “Delay-dependent guaranteed cost control for uncertain stochastic fuzzy systems with multiple time delays,” Progress in Natural Science, vol. 17, no. 1, pp. 95–102, 2007. View at Publisher · View at Google Scholar
  37. H. G. Zhang, M. Li, J. Yang, and D. D. Yang, “Fuzzy model-based robust networked control for a class of nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 39, no. 2, pp. 437–447, 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. X. R. Liu, H. G. Zhang, and J. Dai, “Delay-dependent robust and reliable H∞ fuzzy hyperbolic decentralized control for uncertain nonlinear interconnected systems,” Fuzzy Sets and Systems, vol. 161, no. 6, pp. 872–892, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. H. G. Zhang, Y. H. Luo, and D. Liu, “Neural-network-based near-optimal control for a class of discrete-time affine nonlinear systems with control constraints,” IEEE Transactions on Neural Networks, vol. 20, no. 9, pp. 1490–1503, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. C. Blake, E. Keogh, and C. J. Merz, “UCI repository of machine learning databases,” University of California, Irvine, 1998, http://www.ics.uci.edu/~mlearn/MLRepository.htm.